divNeeded Determines, for every combination of functions, how many species
influence those functions.
divNeeded(overData, type = "positive")Matrix of functions and which species affect them from getRedundancy.
Are the kinds of effects we're looking at "positive", "negative" or "all".
Returns a data frame of all combinations and how many species are needed to influence all of them.
Iterates over all possible combinations of functions. Checks the matrix of which species have positive, negative, or both influences on those functions. Tally's total number of species that have an effect on those functions
data(all_biodepth)
allVars <- qw(biomassY3, root3, N.g.m2, light3, N.Soil, wood3, cotton3)
germany <- subset(all_biodepth, all_biodepth$location == "Germany")
vars <- whichVars(germany, allVars)
species <- relevantSp(germany, 26:ncol(germany))
# re-normalize N.Soil so that everything is on the
# same sign-scale (e.g. the maximum level of a
# function is the "best" function)
germany$N.Soil <- -1 * germany$N.Soil + max(germany$N.Soil, na.rm = TRUE)
res.list <- lapply(vars, function(x) sAICfun(x, species, germany))
names(res.list) <- vars
redund <- getRedundancy(vars, species, germany)
posCurve <- divNeeded(redund, type = "positive")